A Multi-Attention Approach for Person Re-Identification Using Deep Learning

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 7; p. 3678
• Main Authors: Saber, Shimaa, Meshoul, Souham, Amin, Khalid, Pławiak, Paweł, Hammad, Mohamed
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02.04.2023; MDPI
• Subjects: Accuracy; Algorithms; Analysis; Computer vision; Data mining; Datasets; Deep Learning; Discriminant analysis; ECA; Humans; Machine vision; Methods; multi-attention; Neural networks; PAM; person re-identification; Phenotype; Support vector machines; Surveillance; deep learning; ECA; person re-identification; PAM; multi-attention
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23073678

Person re-identification (Re-ID) is a method for identifying the same individual via several non-interfering cameras. Person Re-ID has been felicitously applied to an assortment of computer vision applications. Due to the emergence of deep learning algorithms, person Re-ID techniques, which often involve the attention module, have gained remarkable success. Moreover, people's traits are mostly similar, which makes distinguishing between them complicated. This paper presents a novel approach for person Re-ID, by introducing a multi-part feature network, that combines the position attention module (PAM) and the efficient channel attention (ECA). The goal is to enhance the accuracy and robustness of person Re-ID methods through the use of attention mechanisms. The proposed multi-part feature network employs the PAM to extract robust and discriminative features by utilizing channel, spatial, and temporal context information. The PAM learns the spatial interdependencies of features and extracts a greater variety of contextual information from local elements, hence enhancing their capacity for representation. The ECA captures local cross-channel interaction and reduces the model's complexity, while maintaining accuracy. Inclusive experiments were executed on three publicly available person Re-ID datasets: Market-1501, DukeMTMC, and CUHK-03. The outcomes reveal that the suggested method outperforms existing state-of-the-art methods, and the rank-1 accuracy can achieve 95.93%, 89.77%, and 73.21% in trials on the public datasets Market-1501, DukeMTMC-reID, and CUHK03, respectively, and can reach 96.41%, 94.08%, and 91.21% after re-ranking. The proposed method demonstrates a high generalization capability and improves both quantitative and qualitative performance. Finally, the proposed multi-part feature network, with the combination of PAM and ECA, offers a promising solution for person Re-ID, by combining the benefits of temporal, spatial, and channel information. The results of this study evidence the effectiveness and potential of the suggested method for person Re-ID in computer vision applications.